Evaluating resource efficiency at major copper mines

Minerals Engineering xxx (2016) xxx–xxx

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Minerals Engineering journal homepage: www.elsevier.com/locate/mineng

Evaluating resource efficiency at major copper mines S. Spuerk a,⇑, M. Drobe b, B.G. Lottermoser a a b

Institute of Mineral Resources Engineering, Wuellnerstrasse 2, 52062 Aachen, RWTH Aachen University, Germany Federal Institute for Geosciences and Natural Resources, Hannover, Germany

a r t i c l e

i n f o

Article history: Received 31 July 2016 Revised 21 November 2016 Accepted 5 December 2016 Available online xxxx Keywords: Resource efficiency index Copper mining Resource intensity Analytical hierarchy process

a b s t r a c t Resource efficiency is both, a scientific concept in sustainability assessment and a policy concept that aims to achieve maximum extraction of resource materials from a mineral deposit at minimum waste production. Presently, established proxies for resource efficiency use weight-based measures of a system’s materials consumption However, such proxies are not directly applicable to mining operations. This study introduces a new method and associated techniques for the evaluation and quantification of resource efficiency in mining operations. This approach considers intensities in land, water, energy and mineral deposit consumption (i.e. specific resource consumption to produce one unit of output). Applying this new methodology, resource intensities have been assessed and quantified for 22 major copper mines. Results have allowed relative ranking of these mines in terms of resource efficiency. This work also demonstrates that deposit properties and its geographic location impact on resource efficiency. Consequently, political measures, needed to promote resource efficiency in mining, should focus on region-specific aspects and the properties of the mined ore deposit. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Resource efficiency is a concept that focuses on the responsible consumption of natural resources. In a policy context, the concept aims to use the planet’s limited natural resources in a sustainable manner, while minimising the negative impacts of resource usage on the environment (European Commission, 2011). However, a broadly accepted definition of ‘‘resource efficiency” has not yet been established (Huysman et al., 2015). In general, resource efficiency evaluates the relationship between a systems resource input and its (beneficial) output. Ratios of input and output, such as resource intensities or (their reciprocal values) resource productivities, are commonly used as measures of efficiency. The output of a production system is a product or service, which can be measured using physical or monetary metrics. Expressing the input of natural resources in physical metrics (e.g. weight or volume) is a well-established approach. The suitability of metrics and indicators depends on the chosen system boundaries and research perspective (e.g. global, domestic, company or product). As a provisional lead indicator for comparing resource efficiency of countries, the EU uses Gross Domestic Product (GDP) divided by Domestic Material Consumption (DMC), while admitting the need for developing more suitable indicators ⇑ Corresponding author.

(European Commission, 2011). The DMC approach aggregates different materials based on their mass, which represents the use of implicit weighting (equal importance of mass). To overcome this arbitrary weighting, environmental weighting of material consumption has been proposed (van der Voet et al., 2005). The World Resources Forum discusses the development of a resource efficiency index of nations considering materials, water and land indicators combined by explicit weighting (Tukker et al., 2015). In the manufacturing industry the assessment of resource efficiency is frequently associated with energy and material efficiency (e.g. Kitajima et al., 2015; VDI, 2016), following the rationale that reducing material consumption will reduce stress on natural resources in the upstream supply chain. In mining, resource intensities such as water and energy intensities are established indicators to characterise resource efficiency. In the literature, intensities for copper mining (Northey et al., 2013) as well as for gold and uranium mining (Mudd, 2010) have been reported. A growing database on the energy consumption in the comminution of gold and copper ores allows benchmarking of this isolated process while incorporating grind size and ore grades (Ballantyne and Powell, 2014). Expressing resource intensities separately by category of natural resources or individually for certain processes provides a good overview of different dimensions and drivers of resource efficiency, but it does not allow a ranking of entire mines, unless there is a real dominance relationship. A generally accepted index on resource

E-mail address: [email protected] (S. Spuerk). http://dx.doi.org/10.1016/j.mineng.2016.12.005 0892-6875/Ó 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Spuerk, S., et al. Evaluating resource efficiency at major copper mines. Miner. Eng. (2016), http://dx.doi.org/10.1016/j. mineng.2016.12.005

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S. Spuerk et al. / Minerals Engineering xxx (2016) xxx–xxx

efficiency in the mining industry has yet to be established. The aim of this project is to develop a method for aggregating an index on resource efficiency, allowing a ranking of mines or technical design alternatives within a mining operation, based on a single score. The calculation method needs to be comprehensible and easily accessible, in order to be widely recognised. Additionally, it should enable mining companies to derive operational objectives from the calculated scores.

WRRI ResourceIntensities energy intensity, land intensity, deposit intensity, water intensity

Annualized Inventory Flows metal production, water withdrawal, energy consumption, …

2. Method development The method used in this study assesses the relative efficiency of mines by comparing them to other mines that produce the same main metal commodity. The study considers extraction and mineral processing activities, as those processes are typically performed on site. Thus the mine site defines the system boundary of this assessment. A concentrate is usually the first tradable product in the process chain. Consequently, this material was set to be the standard output. Allocation adjustments were made to incorporate differing forms of products (e.g. cathodes) and byproducts. As mining takes place at the beginning of a product’s life cycle, the perspective can be classified as ‘‘cradle-to-gate”. Resource efficiency indicators used in this work focus on direct inputs. The major advantage of input orientation is its increased measurability, when compared to concepts focusing on subsequent outcomes (impacts) in the causality chain (Geibler et al., 2016). According to the framework presented by Huysman et al. (2015) this approach can be classified as ‘‘resource efficiency at flow level”. The primary benefits of the mining process are considered to be proportional to the amount of valuable content in the products (e.g. metal content in concentrate). Physical mine production was used instead of product value for two reasons. Firstly, mine production is a more constant measure, while product value changes significantly during economic cycles in commodity prices. Secondly, it enables the application of the indicators in further modelling like life-cycle assessment. The method developed in this study combines technical indicators, which are publicly available, with expert opinions (panel method with explicit weighting) on the relevance of each resource category. The indicators considered are based on inventory flows, as these are accessible, measurable, almost uniformly understood worldwide and well-established in the mining industry. The method stops modelling at an early midpoint and does not examine environmental impact categories or the stress on natural resources, thus leaving such considerations of further effects to the panel judgement. When considering more than one criterion weighting is unavoidable, while explicit weighting is superior to implicit weighting (Huppes et al., 2012). The general structure for aggregating an index is presented in the information pyramid in Fig. 1. The primary raw data are derived from measurements usually carried out by or on behalf of the mining company. Processing these data yields annualised inventory flows. Information disclosed in sustainability reports is usually on the level of inventory flows or on the level of mathematically manipulated inventory flows, which makes them indicators. For the numerical aggregation of resource intensity indicators, the Analytical Hierarchy Process (AHP) was applied. It is an established technique for multi-criteria decision making (MCDM) for complex problems (Velasquez and Hester, 2013), which has been broadly applied in environmental analysis of mining operations (e.g. Fukuzawa, 2012; Rikhtegar et al., 2014; Shen et al., 2015). It helps to structure and analyse a problem, by breaking it down to simple pairwise comparison judgements and tests on the consistency of the judgement. The general principle and the mathematical background of AHP can be found in Saaty and Vargas (2012). As

Measured Primary Raw Data

Fig. 1. Information pyramid for evaluating resource efficiency (WRRI = Weighted Relative Resource Intensity).

the aim of the study was to evaluate resource efficiency in mining, resulting in a ranking of mines, there is no direct decision making associated with the analysis (i.e. choosing the most resource efficient mine for sourcing of copper concentrate). However, the ranking of mines provides a basis to support further decision making. In this study, resource intensity RIij of a mine i in resource category j has been defined as the quotient of resource consumption RCij over metal production Pi (Eq. (1)). Metal production in the scope of this assessment refers to metal content in concentrate. For polymetallic mines metal equivalents of the primary metal are applied, using allocation by long term (e.g. 5 years) economic values.

RC ij ¼ RIij Pi

ð1Þ

Resource intensities (RI), which are measured in diverse physical metrics, require normalisation in order to yield compatible values (on a common scale). Normalised RI are obtained by dividing the specific RI by the weighted arithmetic mean of RI over all mines i = 1,. . .,n, as seen in Equation (2)). This normalised value can be considered to be the relative resource intensity RIij,rel., expressed in percent of the peer groups’ average value. The weighted arithmetic mean is obtained by weighting RI by mine production. Taking mine production into account for weighting, sets the average of total production as the benchmark, thus reducing sensitivity of the results towards the incorporation of additional (small) mines into the scope of the assessment.

RIij;rel: ¼

RIj ¼

RIij RIj

Pn Pn RC ij i¼1 RIij  P i Pn ¼ Pi¼1 n i¼1 P i i¼1 P i

ð2Þ

ð3Þ

The score of the index, which is denoted as Weighted Relative Resource Intensity (WRRI), is the sum of the relative resource intensities multiplied by their respective weighting factor wj. Based on AHP, the weighting factors are derived from the principle eigenvector of a pairwise comparison matrix. As they are normalized, the weighting factors sum up to one.

WRRIi ¼

X wj  RIij;rel:

ð4Þ

j

X wj ¼ 1

ð5Þ

j

Please cite this article in press as: Spuerk, S., et al. Evaluating resource efficiency at major copper mines. Miner. Eng. (2016), http://dx.doi.org/10.1016/j. mineng.2016.12.005

S. Spuerk et al. / Minerals Engineering xxx (2016) xxx–xxx

Resource intensities in energy, water, land and mineral deposits have been identified to be relevant categories to be included in the index. As the Global Reporting Initiative (GRI) framework has become a de facto standard in the mining industry (Fonseca et al., 2014), GRI indicators have been applied where possible. While the indicators on direct and indirect energy consumption (G4-EN3, EN4, EN5) as well as on total water withdrawal (G4-EN8) are part of the GRI specific standard disclosure (GRI, 2013b), the amount of land disturbed (G4-MM1) is a sector specific indicator for the mining and metals sector (GRI, 2013a). Depletion of mineral deposits is not covered by the GRI framework, nor is it commonly addressed in sustainability reporting. The absence of an adequate indicator, accounting for mineral depletion and future scarcity, is subject to criticism on the GRI framework (Fonseca et al., 2014). Some companies report the amount of ore extracted for covering (G4-EN1) the indicator on ‘‘materials used by weight” (mass). However, evaluating resource efficiency in mining by means of material input indicators is inappropriate. It is the content of valuable elements in-situ that needs to be exploited efficiently. Deposit depletion in this context does not refer to global depletion of deposits as this is not a fixed stock problem, but to the depletion of the particular deposit under investigation. For the purpose of this study, the indicator on deposit depletion accounted for losses during extraction and processing. The indicator can be stated as the required amount of metal content in-situ to produce one unit of metal content in concentrate. It is conveniently calculated as the reciprocal value of the total recovery (mining and processing). A more specific indicator for deposit depletion would also incorporate the amount of low grade ore wasted or made inaccessible through the mining operation; however, that information is not readily available. The weighted sum of these normalised resource intensities in water (W), energy (E), land (L) and deposits (D) is the index, which is proposed by this study for evaluating resource efficiency of mining operations. Eq. (6) shows the composition of the index (derived from the general form in Eq. (4)) for the selected categories with j = E, W, L, D.

WRRIi ¼ RIiE;rel  wE þ RIiW;rel  wW þ RIiL;rel  wL þ RIiD;rel  wD

ð6Þ

3. Application of the method to major copper mines The application of this WRRI index, which allows quantification of resource efficiency, was explored by evaluating a series of operating copper mines. For this purpose, inventory flow data were collected and weighting factors were calculated from group judgements expressed in questionnaires. 3.1. Data collection and mathematical manipulation The main data used in this work was obtained from sustainability and annual reports as well as from ore reserve and mineral resources reports provided by the respective company. While reporting on mineral resources refers to professional codes and can therefore be regarded as standardised, the quality and coverage of sustainability reports vary significantly. Even the application of the GRI reporting framework does not guarantee consistent and complete data sets on environmental aspects. The reports analysed refer to the years 2011 through 2015. If information in a more recent report differed from a previous one, it was assumed to be an amendment. In this case always the latest information was used. This also applied to discontinued data series. If information for a certain year was missing, the most recent value was used as a proxy. Table 1 shows an exemplary calculation of the relative

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resource intensities, as described in Eqs. (1)) and 2), for the ‘‘Los Bronces” mine, Chile. In 2014, copper production amounted to 404,500 t. Allocation by economic value (five-year average) was applied to account for the additional production of 3811 t of molybdenum. The average recovery of copper in the processing plants was 74% in 2012 (Moore, 2012), which was used as an estimate as it was the most recent information available. As there was no information available on ore loss within the mine for most cases, a 3% loss was estimated for all open pit mines. This resulted in an overall recovery of 71.8%, leading to a reciprocal value, the mineral deposit intensity (copper in the deposit above the cut-off grade per tonne of copper produced) of 1.39. This is slightly above the average intensity of 1.32, which leaves Los Bronces with a relative intensity for deposit utilisation of 105%. Energy intensity is practically the same as the average of the peer group, while water and land intensities are significantly below average. In total 22 major copper mines, accounting for 30% of the world production were assessed. Data on their relative RE are presented in Table 2. The weighted arithmetic mean of this sample was 24.7 GJ/t for energy intensity, 70.8 m3/t for water intensity, 150.3 m2/t for land intensity and 1.33 for deposit intensity. These values were also used as default values for further calculation, in case data for a certain resource category and mine were not available.

3.2. Determining weighting factors In addition to the relative resource intensities of the copper mines, a set of weighting factors is required to calculate index values. This study used a questionnaire based survey, to obtain weighting factors through pairwise comparison (based on AHP). A major survey was conducted among international mining experts (46 experts from 15 countries), and results of this ongoing work demonstrate that water withdrawal was considered to be most important, followed by energy consumption, deposit depletion and land use. The weighting factors applied by this study are the arithmetic mean of the individual expert opinions: water withdrawal (wW: 37%), energy consumption (wE: 24%), deposit depletion (wD: 24%) and land use (wL: 15%).

3.3. Results The WRRI scores for selected copper mines, as shown in Fig. 2, were calculated according to Equation (6)). The results show considerable differences in the resource intensity for the copper mines examined. Los Pelambres is found to be the most resource efficient mine within the sample population. For a majority of the mines moderate scores can be observed. Partly this may be explained by equalization of extreme values in one resource category through contrarian values in the remaining categories. The score of the El Salvador mine stands out, indicating an exceptionally high resource intensity. Besides a high energy intensity, it is the extremely high water intensity (in combination with the high weighting factor for water) that causes its relative poor standing. A tendency of smaller mines to reach lower resource efficiency levels can be seen in the results, as four of the fife mines with the highest scores are among the fife mines with the lowest production rates within the study. However, the position of the Chuquicamata and Radomiro Tomic mines within the ranking sequence has to be questioned. Sulfide ore from the Radomiro Tomic mine is processed in the Chuquicamata plant and hence, water and energy consumption might not have been correctly allocated.

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Table 1 Resource intensities of Los Bronces mine 2014 (Anglo American plc, 2015). Energy RC RI RT RIrel. (%)

Water

Land 3

10,053,626 GJ 24.03 GJ/t 24.67 GJ/t 97.4

22,420,000 m 53.60 m3/t 70.84 m3/t 75.7

Deposit 2

47,100,000 m 112.59 m2/t 150.26 m2/t 74.9

– 1.39 1.32 104.8

Table 2 Relative resource intensities of selected copper mines. Relative energy intensity (RIE,rel.)

Relative water intensity (RIW,rel.)

Relative land intensity (RIL,rel.)

Relative deposit intensity (RID,rel.)

0.814 1.657 0.921 2.701 0.768 1.470 0.957 0.974 0.596 1.669 1.296 0.844 0.692 1.378 0.699 0.832 2.192 1.180 0.999 0.939 2.484 1*

1.977 2.339 0.983 1.875 1.341 1.535 0.629 0.757 0.660 1.194 0.773 0.839 0.561 0.293 0.986 1.455 0.857 0.539 0.565 0.369 5.872 0.842

1* 1* 1.840 2.557 1* 2.739 1* 0.749 0.636 3.620 1.743 0.822 1* 0.325 0.636 1.426 1* 1* 0.879 1* 1* 1*

1* 1* 0.908 0.996 1* 1* 1* 1.048 0.863 1* 1.125 1.111 1* 1* 0.945 0.920 0.980 1.069 0.940 1* 1* 0.826

Andina Chuquicamata Collahuasi El Soldado El Teniente Ernest Henri Gabriela Mistral Los Bronces Los Pelambres Mantos Blancos Mantoverde Minera Escondida Ministro Hales Mt Isa Neves-Corvo OK Tedi Mine Palabora Pampa Norte Prominent Hill Radomiro Tomic Salvador Tenke Fungurme *

Set to default value.

300% 250% 200% 150%

deposit

100%

land

50%

water energy

0%

Fig. 2. Ranking of selected copper mines according to their resource efficiency, with their respective WRRI (Weighted Relative Resource Intensity) values.

4. Discussion 4.1. Method The AHP based method introduced in this paper represents a logic approach for the assessment of resource efficiency in mining. Especially when compared to other techniques for multi-criteria decision making (MCDM) to solve complex problems, the AHP approach has a clear structure. Furthermore, the meaning of the normalised criteria (relative resource intensity) is well deducible and these indicators provide self-contained information. In gen-

eral, a critical interpretation of the scores is inevitable, as quantitative results from MCDM tend to appear more accurate than actually justified by the underlying data. Rank reversal is a known shortcoming of AHP which occurs when new entities are added to the assessment (Saaty and Vargas, 2012). In the case of evaluating mines, there is a finite number of existing mines, so a greater coverage reduces the occurrence of this problem. Another well-established method for efficiency measurement is the data envelopment analysis (DEA) (Geissler et al., 2015). Its main application lies in the field of operations research and economics, but it has also been adopted for the assessment of ecolog-

Please cite this article in press as: Spuerk, S., et al. Evaluating resource efficiency at major copper mines. Miner. Eng. (2016), http://dx.doi.org/10.1016/j. mineng.2016.12.005

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ical efficiency (Dyckhoff and Allen, 2001). As it is a non-parametric method, it does not require prescribed weights. It evaluates the technical efficiency of a set of comparable peers by determining their distance from the efficient production frontier (Geissler et al., 2015). Implicit weights result from the hypothesised technical production function. The problem with applying this method to the assessment of resource efficiency of mining operations is the need for comparability of peers. As deposit characteristics predetermine the possible performance of a mine significantly, it cannot be expected that two mines share the same production function. However, the primary difference between the DEA and the AHP based method introduced in this paper, is the type of efficiency it is measuring. DEA determines technical efficiency, though not all technically efficient solutions are desirable from a society’s point of view, which is the main focus of the resource efficiency concept. As multiple mines aggregately contribute to the overall resource efficiency of a superordinate system (e.g. country) it is their actual contribution that matters, not individual technical efficiency. This looks quite differently when it comes to identifying and setting achievable efficiency goals for operations. The application of DEA (or related methods like SFA) is a promising approach when comparability of units is assured through selection of peers or specific indicators. A general shortcoming of utilising data from sustainability reports is their retrospective nature (Santero and Hendry, 2016), only providing a snap-shot of a continuous process. Taking into account, that different phases of a mine-life have their inherent performance (Castilla-Gómez and Herrera-Herbert, 2015), the need of temporal contextualisation becomes apparent. While averaging performance over multiple years may clear short-term fluctuation, a holistic assessment would require a prediction of average life-ofmine values. 4.2. Indicators The proposed four categories of natural resources (i.e. water withdrawal, energy consumption, land use, deposit depletion) do not represent the entire effect of mining on natural resources, but they are able to cover a great share of it, while preserving measurability. As indicators for AHP need to be independent of each other (which is not entirely true, even for these four indicators), adding additional indicators would require a revision of the whole set of indicators. The GRI indicators used in this study, do not consider different qualities of natural resources. Different sources of energy or fresh water are disregarded, as well as different quality of land or the degree of disturbance. As mentioned before, material use indicators, which are commonly referred to in resource efficiency evaluations in other industries are not applicable to mining. Considering materials input in mining by mass or volume without accounting for material properties like grades appears to be inappropriate for several reasons and fails to deliver operational objectives for the mining industry. Particularly in open pit mines, extracted ore and waste rock outweigh any other material by far, thus they are the de facto only material flows covered by such an indicator. Reducing ore intensity of the processing plant without considering grades or qualities would lead to high-grading and accelerated resource (deposit) depletion. This leaves all ore losses during extraction disregarded, and it does not take into account that it is the metal content rather than the amount of ore that defines a mineral deposit. Thus the loss of valuable content should be reduced, not the amount of ore. When it comes to waste rock, environmental impacts do not result from the pure mass of rock extracted; it is rather its characteristics (e.g. acid forming potential) and the area disturbed which are relevant. The total amount of material (ore and waste) extracted, might be an order of magnitude estimate for the overall

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environmental impact of a mining operation, whereas demanding a reduction of material use in mining yields misleading objectives and overlooks the relevant factual connections. 4.3. Limitations of this study Limitations of this study are mainly related to the quality of input data and the suitability of weighting factors. As there is no standardised means of on-site measurement, data quality depends on definitions, preselection and interpretations on which companies base their disclosures. As the GRI framework is not a technical guideline with strict definitions, it does not guarantee coherence and compatibility of the indicators provided. As it was seen in the results of this study, correct allocation of data within organisations has to be questioned. Another limit of this study results from low availability of land use und recovery data, which was addressed by setting those values to default in the assessment. Therefore, reliable ranking statements are only possible for those mines with a complete indicator set available. An improvement of coverage and quality of disclosed data could not be observed for the period of investigation (2011–2015). Even though it is stated, that mining companies are facing big challenges when aggregating data across geographic locations (Fonseca et al., 2014) and the GRI manual cautions against the loss of meaning by aggregating data (GRI, 2015), several mining companies only report organisation-wide resource consumption. Particularly in mining, where the sale of assets (entire sites) is rather a routine than an exception and commodity prices are highly volatile, nondisclosure of disaggregated, site specific data camouflages relevant contexts. By utilising explicit weighting, this study avoided any misinterpretations associated with the seemingly impartiality of implicit weighting. Full transparency on the influence of weights and on the limits of their validity is crucial. Subjectivity of judgements and personal expertise within the panel are critical issues when utilising explicit weighting. In the questionnaire based survey, this may be partially overcome by a large sample size. When contrasting the weighting factors of this survey with other panel-based weighting factor sets available in literature (Lippiatt, 2007; Huppes et al., 2012), differences between the results are apparent. As those studies have had a different focus and originally used a different set of categories they were not strictly comparable and consequently results and implications from these studies did not apply to this work. The only common ground is presumably, that none of the resource categories are seen as irrelevant, while energy consumption (‘‘climate change”) is seen to have the highest continuous importance across all studies. However, the three additional weighting factor sets do not focus on mining directly, while they originate from a differing temporal and geographic context. There is a need for representative data across geographic locations, in order to derive well justified weights. Rather than resulting in a global consensus this may lead to region specific weights reflecting local scarcity and quality of different natural resources, as these are aspects that cannot be fully considered in the indicators. Whenever applying the WRRI method for only one geographic location (e.g. evaluating technical design alternatives) only local weighting factors should be used. Extensive data on individual judgements would also allow the application of stochastic approaches. 5. Conclusions Calculating weighted relative resource intensity values for some selected copper mines has allowed some first insights into likely

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resource efficiency in mining. The proposed method for evaluating resource efficiency in mining is based on AHP and generates an explicitly weighted relative resource intensity value. Such an index allows ranking of mining operations in terms of resource efficiency and may support selection of resource efficient technologies. In copper mining the apparent differences in resource intensity, their link to deposit properties and local environmental conditions, as well as economic implications need further investigation. In order to produce a generally recognised and less contestable rating, site specific quantitative data have to be presented in sustainability and annual reports by mining companies. Furthermore, a consensus on weighting individual data sets and different parameters has to be found so that globally recognised resource efficiency indices can be established. Acknowledgements The authors would like to acknowledge the support and funding of the BGR (German Federal Institute for Geosciences and Natural Resources) and discussions on this project with Dr Gudrun Franken (BGR) and Dr Jürgen Vasters (BGR).

References Anglo American plc, 2015a. Ore Reserves and Mineral Resources Report 2014. . Anglo American plc, 2015b. Sustainability Report 2014: Negocio cobre. . Anglo American plc, 2016a. Annual Report 2015. . Anglo American plc, 2016b. Ore Reserves and Mineral Resources Report 2015. . Antofagasta plc, 2012. Sustainability Report 2011. . Antofagasta plc, 2013. Sustainability Report 2012. . Ballantyne, G.R., Powell, M.S., 2014. Benchmarking comminution energy consumption for the processing of copper and gold ores. Miner. Eng. 65, 109– 114. BHP Billiton, 2014. Annual Report 2014. . BHP Billiton Chile, 2016. Sustainability Report 2015. . Castilla-Gómez, J., Herrera-Herbert, J., 2015. Environmental analysis of mining operations: dynamic tools for impact assessment. Miner. Eng. 76, 87–96.

Appendix A Base data taken from company reports: Anglo American plc (2015a), Anglo American plc (2015b), Anglo American plc (2016a), Anglo American plc (2016b), Antofagasta plc (2012), Antofagasta plc (2013), BHP Billiton (2014), BHP Billiton Chile (2016), Codelco (2016a), Codelco (2016b), Collahuasi (2016), Freeport-McMoRan Inc. (2016a), Freeport-McMoRan Inc. (2016b), Lundin mining (2013), Lundin mining (2015), Lundin mining (2016), Ok Tedi Mining Limited (2013), Ok Tedi Mining Limited (2015), OZ Minerals Limited (2016a), OZ Minerals Limited (2016b), Palabora Mining Company Limited (2013) and Xstrata Copper North Queensland Operations (2013).

Andina Chuquicamata Collahuasi El Soldado El Teniente Ernest Henri Gabriela Mistral Los Bronces Los Pelambres Mantos Blancos Mantoverde Minera Escondida Ministro Hales Mt Isa Neves-Corvo OK Tedi Mine Palabora Pampa Norte Prominent Hill Radomiro Tomic Salvador Tenke Fungurme

Energy consumption [PJ]

Water withdrawal [Mm3]

4.92 14.49 10.73 2.16 9.43 1.80 2.95 10.05 6.74 2.16 1.66 24.66 4.07 5.77 1.32 5.02 4.85 7.28 3.77 7.41 3.16

34.30 58.74 33.02 4.30 47.27 5.40 5.57 22.42 22.00 4.43 2.84 70.42 9.46 17.19 5.34 64.19 5.44 9.54 6.13 8.36 21.46 16.20

Land disturbed [ha]

13,108 1245

Recovery [%]

85.44 77.94

2044 4710 4380 2850 1357 14,478 1542 731 2709

2023

74.07 89.90 74.65 69.85

88.50 84.40 85.40 72.62 89.00

94.00

Production Cu [t]

Production CuEq [t]

224,264 308,625 455,300 36,000 471,157 34,106 125,009 404,500 403,700 52,400 51,800 1,152,510 238,305 315,369 51,369 75,901 89,631 826,220 130,305 315,747 48,582 204,000

244,996 354,451 474,088 36,000 497,724 49,661 125,009 418,320 458,341 52,400 51,800 118,470 238,305 315,369 76,467 126,463 89,631 826,220 153,128 320,012 51,585 271,692

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S. Spuerk et al. / Minerals Engineering xxx (2016) xxx–xxx Codelco, 2016a. Annual Report 2015. . Codelco, 2016b. Indicadores de sustentabilidad. . Collahuasi, 2016. Sustainability Report 2015. . Dyckhoff, H., Allen, K., 2001. Measuring ecological efficiency with data envelopment analysis (DEA). Eur. J. Oper. Res. 132, 312–325. European Commission, 2011. Roadmap to a Resource-Efficient Europe: COM(2011) 571. Fonseca, A., McAllister, M.L., Fitzpatrick, P., 2014. Sustainability reporting among mining corporations: a constructive critique of the GRI approach. J. Clean. Prod. 84, 70–83. Freeport-McMoRan Inc. 2016a. Annual Report 2015. . Freeport-McMoRan Inc. 2016b. CDP 2016 Water Disclosure. . Fukuzawa, R., 2012. Climate change policy to foster pollution prevention and sustainable industrial practices – a case study of the global nickel industry. Miner. Eng. 39, 196–205. Geibler, J.von., Cordaro, F., Kennedy, K., Lettenmeier, M., Roche, B., 2016. Integrating resource efficiency in business strategies: a mixed-method approach for environmental life cycle assessment in the single-serve coffee value chain. J. Clean. Prod. 115, 62–74. Geissler, B., Mew, M.C., Weber, O., Steiner, G., 2015. Efficiency performance of the world’s leading corporations in phosphate rock mining. Resour. Conserv. Recycl. 105, 246–258. GRI, 2013a. G4 Mining and Metals Sector Disclosures. . GRI, 2013b. G4 Sustainability Reporting Guidelines: Reporting Principles and Standard Disclosures. . GRI, 2015. G4 Implementation Manual. . Huppes, G., van Oers, L., Pretato, U., Pennington, D.W., 2012. Weighting environmental effects: analytic survey with operational evaluation methods and a meta-method. Int. J. Life Cycle Assess. 17 (7), 876–891. Huysman, S., Sala, S., Mancini, L., Ardente, F., Alvarenga, R.A., de Meester, S., Mathieux, F., Dewulf, J., 2015. Toward a systematized framework for resource efficiency indicators. Resour. Conserv. Recy. 95, 68–76. Kitajima, T., Sawanishi, H., Taguchi, M., Torihara, K., Honma, O., Mishima, N., 2015. A proposal on a resource efficiency index for EEE. Proc. CIRP 26, 607–611. Lippiatt, B.C., 2007. BEES 4.0: Building for Environmental and Economic Sustainability Technical Manual and User Guide. . Lundin mining, 2013. Technical Report for Neves-Corvo Mine and Semblana Deposit, Portugal. .

7

Lundin mining, 2015. sustainability report 2014. . Lundin mining, 2016. Annual Information Form 2015. . Moore, P., 2012. Copper capital. International Mining (April), 14–19. . Mudd, G.M., 2010. The Environmental sustainability of mining in Australia: key mega-trends and looming constraints. Resour. Policy 35 (2), 98–115. Northey, S., Haque, N., Mudd, G., 2013. Using sustainability reporting to assess the environmental footprint of copper mining. J. Clean. Prod. 40, 118–128. Ok Tedi Mining Limited, 2013. Annual Review 2012. . Ok Tedi Mining Limited, 2015. Annual Review 2014. . OZ Minerals Limited, 2016a. Annual Report 2015. . OZ Minerals Limited, 2016b. Sustainability Report 2015. . Palabora Mining Company Limited, 2013. Integrated annual report 2012. . Rikhtegar, N., Mansouri, N., Ahadi Oroumieh, A., Yazdani-Chamzini, A., Kazimieras Zavadskas, E., Kildiene˙, S., 2014. Environmental impact assessment based on group decision-making methods in mining projects. Econ. Res.-Ekonomska Istrazˇivanja 27 (1), 378–392. Saaty, T.L., Vargas, L.G., Models, Methods, 2012. Concepts & Applications of the Analytic Hierarchy Process. . second ed. International Series in Operations Research & Management Science second ed., vol. 175 Springer, New York. Santero, N., Hendry, J., 2016. Harmonization of LCA methodologies for the metal and mining industry. Int. J. Life Cycle Assess. Shen, L., Muduli, K., Barve, A., 2015. Developing a sustainable development framework in the context of mining industries: AHP approach. Resourc. Policy 46, 15–26. Tukker, A., Guinee, J., van Oers, Lauran, van der Voet, E., 2015. Towards a resource efficiency index of nations. Discussion paper. . van der Voet, E., van Oers, L., Moll, S., Schütz, H., Bringezu, S., Bruyn, S.d., Sevenster, M., Warringa Geert, 2005. Policy Review on Decoupling: Development of indicators to assess decoupling of economic development and environmental pressure in the EU-25 and AC-3 countries. . Velasquez, M., Hester, P.T., 2013. An analysis of multi-criteria decision making methods. Int. J. Oper. Res. 10 (2), 56–66. . Verein Deutscher Ingenieure e.V. (VDI), 2016. Resource efficiency: Methodological principles and strategies 03.100.01, 13.020.01. Beuth, Düsseldorf. Xstrata Copper North Queensland Operations, 2013. Sustainability Report 2012. .

Please cite this article in press as: Spuerk, S., et al. Evaluating resource efficiency at major copper mines. Miner. Eng. (2016), http://dx.doi.org/10.1016/j. mineng.2016.12.005